Alarm systems balance the requirements of minimizing false alarms against minimizing detection failures. It is desirable to minimize false alarms to reduce the associated nuisance and costs and to minimize detection failures to maintain the deterrent and detection value of the alarm system.
Alarm detection techniques include various switches, motion detectors, glass-break detectors, vibration detectors, infrasound detectors and other techniques. These techniques do not discern the detected activity of an intruder from other detected activities. In fact, the relatively infrequent occurrence of intruder activity results in a high potential for false alarms.
Because present day detectors do not discern intruders from occupants, alarm systems have made the assumption that occupants will modify their behavior to prevent false alarms. The frequent occurrence of false alarms has proven this assumption to be incorrect. Statistics from the public sector and intruder alarm industry indicate that more than 99% of intruder alarm responses may be false and attributed to occupants. This high rate of false alarms is costly to alarm owners, monitoring companies, and police authorities. Such statistics also indicate that alarm systems fail to detect some 30% of intruder occurrences. However, alarm systems are considered to be effective in preventing intrusions attributed to deterrence. Locations with intruder alarm systems exhibit significantly fewer intrusions than locations without alarm systems.
The most effective way to minimize false alarms and detection failures is to include intrinsic intelligence that enables alarm systems and detectors to discern intruders from occupants. Such intrinsic intelligence continuously modifies the response of alarm systems and detectors to detected activities. Artificial intelligence techniques may be employed to provide such intrinsic intelligence. Unlike present day alarm systems that reduce false alarms by minimizing the sources of information, artificial intelligence minimizes false alarms and detection failures by increasing the sources of information thereby improving the decision process. Such information may be provided by a multiplicity of detectors within an alarm system and certain detector technologies.
One such detector technology may be infrasound detection. Infrasound is generally considered to be sub-audible sound with frequencies less than 20 Hz. Infrasound signals inherently contain a large amount of information over a broadband and tend to uniformly fill the environment. Typical causes of infrasound include the movement of large mass objects such as windows and doors and even the flexing of walls, floors and ceilings.
FR 2569027 describes an intruder alarm based on detection of pressure waves in the frequency range below 10 Hz, different frequencies in this range being analyzed and compared, in order to avoid false alarms. An early form of digital signal process (DSP) is used. A series of band-pass filters is defined for separating the signal into various frequency components. Fourier analysis is used to determine various signal parameters. The purpose of the Fourier analysis is to remove undesirable frequencies from the detected signal, and then determine whether the signal is from a singular event (such as a door opening/closing) or an ongoing noise (such as wind). This technique is commonly used in motion detectors.
WO 90/11586 also describes an intruder alarm with detection of pressure waves in a low frequency range, similarly to FR 2569027. However, WO 90/11586 presents an improved frequency filtering system to limit the bandwidth of the detected signal.
The prior art of alarm systems and detectors has mostly tried to improve the ergonomics or the user control interface and reduce spurious alarm responses. As such, present day alarm technologies respond to the presence or absence of a signal without discerning the probable cause of the signal.
In summary, it is generally accepted that alarm systems are effective with the existing rate of detection failures. However, present-day alarm systems and detectors do not discern intruder activities from other activities thereby causing frequent false alarms that reduce the value of the alarm system. It is proposed to employ a processor and software algorithms to comprise an artificial intelligence system for use with intruder and vehicle alarm systems and various detector technologies. Such an artificial intelligence system may discern intruder activities from occupant and other activities thereby reducing false alarms and detection failures. It is also proposed to employ such artificial intelligence system with infrasound detection technology in a manner that may provide comprehensive perimeter detection.